Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,295 Bytes
932ae62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import torch
import nodes
import comfy.utils
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
azimuth = torch.as_tensor([azimuth])
embeddings = torch.stack(
[
torch.deg2rad(
(90 - elevation) - (90)
), # Zero123 polar is 90-elevation
torch.sin(torch.deg2rad(azimuth)),
torch.cos(torch.deg2rad(azimuth)),
torch.deg2rad(
90 - torch.full_like(elevation, 0)
),
], dim=-1).unsqueeze(1)
return embeddings
class StableZero123_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
cam_embeds = camera_embeddings(elevation, azimuth)
cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
class StableZero123_Conditioning_Batched:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
cam_embeds = []
for i in range(batch_size):
cam_embeds.append(camera_embeddings(elevation, azimuth))
elevation += elevation_batch_increment
azimuth += azimuth_batch_increment
cam_embeds = torch.cat(cam_embeds, dim=0)
cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
class SV3D_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
azimuth = 0
azimuth_increment = 360 / (max(video_frames, 2) - 1)
elevations = []
azimuths = []
for i in range(video_frames):
elevations.append(elevation)
azimuths.append(azimuth)
azimuth += azimuth_increment
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
"SV3D_Conditioning": SV3D_Conditioning,
}
|